feedforwardfunktioner

Feedforwardfunktioner, also known as feedforward networks, are a fundamental type of artificial neural network. In a feedforward network, the information flows in only one direction, from the input layer, through any hidden layers, and finally to the output layer. There are no cycles or loops in the connections. Each neuron in a layer is connected to every neuron in the subsequent layer, but not to neurons in the same layer or in previous layers.

The output of a feedforward function is determined solely by its current input and its weights. Unlike recurrent neural networks, feedforward networks do not have memory of past inputs. They are used for tasks where the output depends only on the current input, such as classification, regression, and pattern recognition. During training, the network adjusts its weights to minimize the difference between its predicted output and the actual target output. This adjustment process typically involves algorithms like backpropagation. Examples of feedforward networks include simple perceptrons and multi-layer perceptrons.